3D Object Extraction for Automatic Threat Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing X-ray imaging-based threat detection systems require significant human intervention for final decision-making, increasing the cost of security systems, despite performing reasonably well in recognizing explosives.
Innovation Solution
An automatic threat detection method and system that extracts 3-D objects from volumetric CT data using multi-stage Segmentation and Carving followed by a Support Vector Machine classifier, reducing the need for human intervention by constructing feature vectors and classifying objects as threats or benign.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human intervention is used for final decision-making in threat detection, then detection accuracy can be maintained through expert judgment, but operational costs and system complexity increase significantly
Solution Approach 1:
The system enables self-service automation where the CT imaging system and classification algorithms autonomously perform threat detection and classification without requiring human operators for final decisions. The automated classification unit processes volumetric data, extracts features, and classifies objects as threats or benign items, making the system self-sufficient in the detection decision-making process.
2Reliability
If human operators are deployed for final threat detection decisions, then detection reliability is maintained, but productivity and processing speed decrease due to manual review requirements
Solution Approach 1:
The patent replaces the mechanical human decision-making process with an automated classification system that uses computer algorithms to analyze volumetric CT data, extract features, and classify objects. This substitution eliminates the need for human operators to manually review each scanned item, thereby maintaining detection reliability while significantly increasing processing speed and productivity.
3Reliability
If extensive human manpower is allocated to threat detection, then detection thoroughness is improved, but operational costs increase significantly
Solution Approach 1:
The automated classification system performs threat detection thoroughness independently without requiring extensive human manpower. The system autonomously processes volumetric data, extracts relevant features, and classifies objects, thereby maintaining high detection thoroughness while eliminating the need for multiple human operators and significantly reducing operational costs.
4Productivity
If automated classification is implemented to reduce human intervention, then productivity and cost efficiency improve, but system complexity and development difficulty increase
Solution Approach 1:
The automated system is divided into distinct functional modules: a volumetric data acquisition module, a feature extraction module that identifies specific characteristics of scanned objects, and a classification module that categorizes objects as threats or benign. This segmentation allows each module to be developed and optimized independently, managing system complexity while achieving high productivity and cost efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution achieves a higher probability of detection with lower false alarms, reducing operational costs and enhancing security by reliably clearing a larger proportion of baggage without the need for extensive human manpower.
Implementation Method 1
extracting at least one 3-D object from the volumetric CT data; constructing a feature vector for each of the at least one 3-D objects
Implementation Method 2
A computed tomography (CT) scan, which uses computer-processed combinations of many X-ray images taken from different angles to produce cross-sectional images of specific areas of a scanned object
Data Source
AI summary
Automatic threat detection of volumetric computed tomography (CT) data, including: extracting at least one 3-D object from the volumetric CT data; constructing a feature vector for each of the at least one 3-D objects; and classifying each 3-D object as one of a threat or benign object using the feature vector and a set of truth threat and benign objects.